The amount of research published to date indicates an increasing interest in the areas of multichannel signal processing. The availability of a wide set of multichannel information sources in application areas, such as color image processing, multispectral remote sensing imagery, biomedicine, robotics and industrial inspection have stimulated a renewed interest in developing efficient and cost effective processing techniques for multichannel signals. In this paper, fuzzy logic and adaptive learning techniques are utilized to derive new filter classes for multichannel signal processing. Using the proposed methodology, multichannel nonlinear problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated results. The fuzzy approach provides insight into the nature of the filtering process and the structure of the underlying nonlinear operator. The special case of color image processing is studied as an important example of multichannel signal processing. Simulation results indicate that the proposed adaptive fuzzy filters are computationally attractive and have excellent performance.